• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

Building the mobile carrier of the future with AI

July 8, 2019   Big Data
 Building the mobile carrier of the future with AI

Presented by fonYou


While we are in the midst of one of the most exciting times in telecommunications history — driven by things like 5G, Wi-Fi 6, virtualization, and transformation of networks – it’s easy to focus on what the future might bring, as opposed to what operator networks are already capable of doing today. The reality is that mobile carriers don’t have to wait for the future to arrive because they are already sitting on top of a veritable goldmine.

The “goldmine” is made up of massive amounts of data that traverse telecom networks. Today’s core networks are made of expensive, heavy-duty equipment optimized for transporting this data. They weren’t designed with data analytics in mind. As a result, most operators haven’t yet developed effective strategies to transform user data flows into actionable customer insights. Let alone take it to the next stage to become analytics-driven, digital businesses that can compete with OTTs on customer intelligence.

For this to happen, operators need to disrupt the existing model and apply machine-learning algorithms to the real-time data which is flowing through their networks today. That would allow them to decode complex event combinations, traffic patterns and smartphone interactions to infer the tribes and behaviors that define each individual customer. Raw data carried over the mobile network could then be transformed into user behavioral data to generate actionable insights for real-time and predictive responses to their customers’ needs and desires.

Mobile carriers to become data-powered businesses

So, where are we today? Mobile carriers typically have access to many different sources of customer information, including standard commercial systems such as CRMs as well as more advanced OSS/BSS platforms designed for carriers. These data sources can include static or near real-time information about customers. The challenge is that with these systems, it’s difficult to assess the current customer context and understand the underlying patterns that drive customer behavior.

However, what if they could “see” that a customer is online and streaming? What if they could see that they are using an app to search travel destinations? Or what if they could discern that a customer’s device is being infected by malware? Understanding the context gives operators boundless opportunities to become a more useful partner or resource for their customers.

Operators are typically well aware that they have to develop into data-driven businesses in order to stay relevant. The good news is that the majority of operators globally have already started on their digital transformation journey. Our only caution to operators is that they need to ensure that people — their customers — are central to any digital transformation project.

Harnessing massive mobile data to understand each customer

The fact is that carriers already have everything they need in their core networks to better understand and serve their customers. The challenge of course is to turn these petabytes of information from users’ interactions with their mobile devices into a real and authentic view of the customer, their behavior, and their needs. Creating something more useful and manageable, something like a self-organized data lake, requires some heavy processing power and speed which almost inevitably leads us to the cloud and AI.

Powered by cloud and AI, this massive, real-time data could be harnessed and transformed into new, valuable knowledge about mobile users. With the right technology in place, this data would be extracted directly from the telecom network, and by applying machine learning algorithms, operators could understand, detect, and predict with a high degree of accuracy what they want or need.

The commercial applications are limitless. An obvious first step would be to present highly targeted offerings for customers, according to their real-time context and profile. Take the example of a customer who regularly uses HBO’s video streaming application, who owns a new 4K smartphone, and is currently connected. If you could discern that they are apparently addicted to whatever the next big thing after Game of Thrones may be, what are the chances that they would pay a sensible price for an early screening of the next episode, ahead of the masses? What about that customer searching an app for vacation destinations? What sort of offers might they be receptive to? And what about that customer whose device is being infected by malware. Maybe you could build some extreme loyalty with a free, pro-active monitoring and malware-isolation service?

Secondly, operators could also improve their commercial efficiency, by acting smarter on the design of their offer portfolio, which could be generated to match real customer consumption habits.

And finally, operators could also better predict future revenue — or Customer Lifetime Value (CLV) — for each customer, which would help with better business planning and strategic targeting of all existing and prospective customer segments. With real-time insights, operators can distinguish between profitable, nearly profitable and unprofitable customer segments, and tailor their marketing and sales strategies accordingly — including unique pricing strategies based on individual customer behavior.

Mobile carriers are sitting on top of a goldmine

Without question, carriers should be excited about the future revenue potential their networks will generate, especially as they deploy 5G networks. However, they should not ignore the wealth of data that is cycling through their networks, which could unleash new customer insights and commercial applications when harnessed by cloud and AI applications.

For too long, operators have been feeling the heat from currently dominant pure digital players. But there is nothing stopping smart carriers from becoming competitive, agile, and fully digital businesses that can thrive. They only need to build it, today.

Fernando Nunez Mendoza is Founder & CEO of fonYou.


fonYou’s iCarrier platform creates new customer knowledge by extracting mobile data usage directly from the carrier network. By applying AI and Machine Learning techniques the iCarrier predicts what customers will need, creating a whole new world of commercial applications. fonYou is building the mobile carrier of the future with AI, today. Ready to join us? visit www.fonyou.com.


Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact sales@venturebeat.com.

Let’s block ads! (Why?)

Big Data – VentureBeat

Building, carrier, future, Mobile
  • Recent Posts

    • Kevin Hart Joins John Hamburg For New Netflix Comedy Film Titled ‘Me Time’
    • Who is Monitoring your Microsoft Dynamics 365 Apps?
    • how to draw a circle using disks, the radii of the disks are 1, while the radius of the circle is √2 + √6
    • Tips on using Advanced Find in Microsoft Dynamics 365
    • You don’t tell me where to sit.
  • Categories

  • Archives

    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited